Mathematical Camera Array Optimization for Face 3D Modeling Application.

Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability needed for image-based 3D face models. This is caused among many factors by the hardware limitations and imperfection of the cameras and the lack of proficiency in designing the ideal camera-system configuration. Accordingly, for precise measurements, we still need engineering-based techniques to ascertain the specific level of deliverables quality. In this paper, an optimal geometric design methodology of the camera network is presented by investigating different multi-camera system configurations composed of four up to eight cameras. A mathematical nonlinear constrained optimization technique is applied to solve the problem and each camera system configuration is tested for a facial 3D model where a quality assessment is applied to conclude the best configuration. The optimal configuration is found to be a 7-camera array, comprising a pentagon shape enclosing two additional cameras, offering high accuracy. For those who prioritize point density, a 9-camera array with a pentagon and quadrilateral arrangement in the X-Z plane is a viable choice. However, a 5-camera array offers a balance between accuracy and the number of cameras.

Fusion of UAV-based infrared and visible images for thermal leakage map generation of building facades.

To make the best use of available energy resources and reduce costs, improving the energy efficiency of buildings has become a critical issue for the construction industry. Today, developing a three-dimensional model of the energy consumption rates in buildings based on thermal infrared images is essential to visualize, identify and increase energy efficiency. The purpose of this study is to suggest a methodology for generating a thermal leakage map of building facades utilizing the fusion of thermal infrared and visible images captured by Unmanned Aerial Vehicles (UAVs). In general, the proposed method involves three basic steps: the generation of thermal infrared and visible dense point clouds from the building's facade using Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms; the fusion of visible and thermal infrared dense point clouds using the Iterative Closest Point (ICP) algorithm to overcome thermal infrared point cloud constraints; the use of edge extraction and region-based segmentation methods to determine the location of the thermal leakage of building facade's. To that end, two datasets obtained for separate building facades are used to assess the proposed strategy. The results of the data analyses for the extraction of the desired components and determination of thermal leakage locations on the building facets provided a Precision and Recall score of 87 and 90% for the first dataset and 87 and 88 for the second dataset. Examining the outcomes of calculating thermal leakage zones indicates improving Precision and Recall.

Encapsulation of ritonavir in solid lipid nanoparticles: in-vitro anti-HIV-1 activity using lentiviral particles.

OBJECTIVES: In this study, ritonavir was entrapped into solid lipid nanoparticles (SLNs) employing two production methods. The prepared SLNs were characterized and antiretroviral activity was investigated for more efficient formulation. METHODS: Ritonavir-loaded SLNs were produced by solvent emulsification evaporation (SE) and double emulsion methods (DE), and the effects of Tween80 and poloxamer188 as external phase surfactant were compared. Prepared SLNs were characterized in terms of size, surface charge, entrapment efficiency (EE), release profile and thermal behaviour. Moreover, the activity of drug-loaded SLNs was investigated on the lentiviral-based pseudo-HIV-1 particles. KEY FINDINGS: The average size of negatively charged SLNs was 170-250 nm with polydispersity index (PDI) of 0.2. The most EE% was about 53.2% achieved by DE method in the presence of poloxamer188. It was found that addition of poloxamer188 in the process led to increased entrapment efficiency and particle size. The in-vitro antiviral experiment showed ritonavir SLNs can actively maintain inhibition of virus production as well as free drug. CONCLUSIONS: In this study, we showed the SLNs not only can encapsulate ritonavir efficiently but also can maintain its antiviral activity and modulate drug release as promising nanocarrier.